Homework 1

Author

Reagan Dietz

Published

September 8, 2024

Load Packages

library(Hmisc)
library(tidyverse)

Problem 1

Survey

Wednesday Sept. 4 at 1:43pm

Campuswire

Insert the image you uploaded to Campuswire here.

This image shows top portion of the editor pane in RStudio with the image icon circled in red

How to insert an image into a Quarto document

Problem 2

Question 1

The study population for data set 1 are people 16 years and older who do not live in communal residences, which makes the study population broad and gives a wide variety of who could be surveyed (men/women, older people/younger people, etc.). The study population for data set 2 are the police officers and their crime records, which is more specific and narrow.

Question 2

The sampling strategy for data set 1 is voluntary. The sampling strategy for data set 2 is convenience sampling because they are using the files that they already have.

Question 3

The sampled population for data set 1 is the 38,000 people who are 16 and older and do not live in communal residences. The sampled population for data set 2 are the prewritten files.

Question 4

The target population of the study are UK residents.

Question 5

The reliability of data set 1 is somewhat reliable as it is self-reported and people may not be completely honest in their answers. The reliability of data set 2 is reliable as it is the crime reports that were created by the police officers. The validity of the study for data set 1 is good because it is a large number of people with a large age range. The validity of the study for data set 2 is good because it is the police records. For data set 1, I think it is generalizable because it covers a large amount of people with a large age range. For data set 2, for data set 2 it is generalizable because it is prewritten records that are known to be true.

Problem 3

Question 1

The <- notation is equivalent to an = sign in R and is often used to declare variables. After running this code chunk, the named dataframe df appears in the environment on the right-hand side of RStudio.

df <- read_csv('https://www.openintro.org/data/csv/babies.csv')
Rows: 1236 Columns: 8
── Column specification ────────────────────────────────────────────────────────
Delimiter: ","
dbl (8): case, bwt, gestation, parity, age, height, weight, smoke

ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

Question 2

The notation Hmisc:: directly calls this function from the Hmisc package. describe() is a common function name, and sometimes this is needed to indicate to R which function from which package you want to use. The pipe feature |> sends the results of the first line directly into the function on the 2nd line and is a convenient way to chain functions together.

This code prints a useful and attractive summary of the data set we are using.

Hmisc::describe(df) |> 
  html()
df Descriptives
df

8 Variables   1236 Observations
case
image
        n  missing distinct     Info     Mean      Gmd      .05      .10      .25 
     1236        0     1236        1    618.5    412.3    62.75   124.50   309.75 
      .50      .75      .90      .95 
   618.50   927.25  1112.50  1174.25
lowest : 1 2 3 4 5 , highest: 1232 1233 1234 1235 1236
bwt
image
nmissingdistinctInfoMeanGmd.05.10.25.50.75.90.95
123601071119.620.33 88.0 97.0108.8120.0131.0142.0149.0
lowest : 55 58 62 63 65 , highest: 169 170 173 174 176
gestation
image
nmissingdistinctInfoMeanGmd.05.10.25.50.75.90.95
1223131060.999279.316.57252.0262.0272.0280.0288.0295.8302.0
lowest : 148 181 204 223 224 , highest: 330 336 338 351 353
parity
nmissingdistinctInfoSumMeanGmd
1236020.573150.25490.3801
age
image
nmissingdistinctInfoMeanGmd.05.10.25.50.75.90.95
12342300.99727.266.50619202326313638
lowest : 15 17 18 19 20 , highest: 41 42 43 44 45
height
image
nmissingdistinctInfoMeanGmd.05.10.25.50.75.90.95
121422190.98664.052.83960616264666768
 Value         53    54    56    57    58    59    60    61    62    63    64    65
 Frequency      1     1     1     1    10    26    55   105   131   166   183   182
 Proportion 0.001 0.001 0.001 0.001 0.008 0.021 0.045 0.086 0.108 0.137 0.151 0.150
                                                     
 Value         66    67    68    69    70    71    72
 Frequency    153   105    54    20    13     6     1
 Proportion 0.126 0.086 0.044 0.016 0.011 0.005 0.001
For the frequency table, variable is rounded to the nearest 0
weight
image
nmissingdistinctInfoMeanGmd.05.10.25.50.75.90.95
1200361050.999128.622.39102.0105.0114.8125.0139.0155.0170.0
lowest : 87 89 90 91 92 , highest: 215 217 220 228 250
smoke
nmissingdistinctInfoSumMeanGmd
12261020.7174840.39480.4782

Question 3

The Child Health and Development Studies investigate a range of topics. One study, in particular, considered all pregnancies between 1960 and 1967 among women in the Kaiser Foundation Health Plan in the San Francisco East Bay area. The variables in this data set are as follows.

Data Dictionary
Variable Name Variable Description Variable Type
case id number categorical, multi-categorical
bwt birthweight, in ounces numerical
gestation length of gestation, in days numerical
parity binary indicator for a first pregnancy (0 = first pregnancy) categorical, binary
age mother’s age in years numerical
height mother’s height in inches numerical
weight mother’s weight in pounds numerical
smoke binary indicator for whether the mother smokes categorical, binary

Question 4

Below, 2 numeric variables were investigated for potential relationships. The independent, explanatory variable I chose is gestation, and the dependent, response variable I chose is bwt.

df |>
  ggplot(aes(x = gestation,  y = bwt))+

  geom_point()
Warning: Removed 13 rows containing missing values or values outside the scale range
(`geom_point()`).

Describe what you see in your plot here.

The gestastion period of 250-300 is the highest, with the average birthweight from 100-150.

Session Info

This portion of the document describes the conditions in RStudio under which this report was created. This is important to include so that work is reproducible by others.

xfun::session_info()
R version 4.4.1 (2024-06-14)
Platform: aarch64-apple-darwin20
Running under: macOS Sonoma 14.5

Locale: en_US.UTF-8 / en_US.UTF-8 / en_US.UTF-8 / C / en_US.UTF-8 / en_US.UTF-8

Package version:
  askpass_1.2.0       backports_1.5.0     base64enc_0.1-3    
  bit_4.0.5           bit64_4.0.5         blob_1.2.4         
  broom_1.0.6         bslib_0.8.0         cachem_1.1.0       
  callr_3.7.6         cellranger_1.1.0    checkmate_2.3.2    
  cli_3.6.3           clipr_0.8.0         cluster_2.1.6      
  colorspace_2.1-1    compiler_4.4.1      conflicted_1.2.0   
  cpp11_0.4.7         crayon_1.5.3        curl_5.2.1         
  data.table_1.15.4   DBI_1.2.3           dbplyr_2.5.0       
  digest_0.6.37       dplyr_1.1.4         dtplyr_1.3.1       
  evaluate_0.24.0     fansi_1.0.6         farver_2.1.2       
  fastmap_1.2.0       fontawesome_0.5.2   forcats_1.0.0      
  foreign_0.8-86      Formula_1.2-5       fs_1.6.4           
  gargle_1.5.2        generics_0.1.3      ggplot2_3.5.1      
  glue_1.7.0          googledrive_2.1.1   googlesheets4_1.1.1
  graphics_4.4.1      grDevices_4.4.1     grid_4.4.1         
  gridExtra_2.3       gtable_0.3.5        haven_2.5.4        
  highr_0.11          Hmisc_5.1-3         hms_1.1.3          
  htmlTable_2.4.3     htmltools_0.5.8.1   htmlwidgets_1.6.4  
  httr_1.4.7          ids_1.0.1           isoband_0.2.7      
  jquerylib_0.1.4     jsonlite_1.8.8      knitr_1.48         
  labeling_0.4.3      lattice_0.22.6      lifecycle_1.0.4    
  lubridate_1.9.3     magrittr_2.0.3      MASS_7.3.60.2      
  Matrix_1.7.0        memoise_2.0.1       methods_4.4.1      
  mgcv_1.9.1          mime_0.12           modelr_0.1.11      
  munsell_0.5.1       nlme_3.1.164        nnet_7.3-19        
  openssl_2.2.1       parallel_4.4.1      pillar_1.9.0       
  pkgconfig_2.0.3     prettyunits_1.2.0   processx_3.8.4     
  progress_1.2.3      ps_1.7.7            purrr_1.0.2        
  R6_2.5.1            ragg_1.3.2          rappdirs_0.3.3     
  RColorBrewer_1.1.3  readr_2.1.5         readxl_1.4.3       
  rematch_2.0.0       rematch2_2.1.2      reprex_2.1.1       
  rlang_1.1.4         rmarkdown_2.28      rpart_4.1.23       
  rstudioapi_0.16.0   rvest_1.0.4         sass_0.4.9         
  scales_1.3.0        selectr_0.4.2       splines_4.4.1      
  stats_4.4.1         stringi_1.8.4       stringr_1.5.1      
  sys_3.4.2           systemfonts_1.1.0   textshaping_0.4.0  
  tibble_3.2.1        tidyr_1.3.1         tidyselect_1.2.1   
  tidyverse_2.0.0     timechange_0.3.0    tinytex_0.52       
  tools_4.4.1         tzdb_0.4.0          utf8_1.2.4         
  utils_4.4.1         uuid_1.2.1          vctrs_0.6.5        
  viridis_0.6.5       viridisLite_0.4.2   vroom_1.6.5        
  withr_3.0.1         xfun_0.47           xml2_1.3.6         
  yaml_2.3.10